A New Time-Frequency Analysis Method of Multi-Component Chirp Signal

2006 ◽  
Author(s):  
Fan Cao ◽  
Shuxun Wang ◽  
Fei Wang
Keyword(s):  
Frequency Analysis ◽  
Chirp Signal ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
New Time

Chirp Signal Time-Frequency Analysis Characteristic Comparison

2012 ◽  
Vol 226-228 ◽  
pp. 568-571 ◽  
Author(s):  
Jing Lei Zhou ◽  
Fan Wang
Keyword(s):  
Frequency Analysis ◽  
Chirp Signal ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Signal Noise ◽  
Time Frequency ◽  
Sine Signal ◽  
Non Stationary Signal ◽  
Signal Time ◽  
Selection Of

Chirp signal is a typical non-stationary signal, and have been widely used in communication, sonar, radar and so on. So, this signal is worth to analysis. In order to show the characteristics, this paper first introduces the definition and formula of each algorithm, then with all kinds of time-frequency analysis method to the signals, and the signal to add two sine signal noise are analyzed, the comparison of the characteristics of the method in the paper, and the signal for the analysis, the selection of an appropriate analysis. Through analysis and comparison, when dealing with the signal, Hilbert-Huang transformation not only has a better gathered characteristic, but also has a better resolution to distinguish the sine signal noise. Finally, use the MATLAB software simulation to obtain the result.

A Nonlinear Time-Frequency Analysis Method

2004 ◽  
Vol 52 (6) ◽  
pp. 1585-1595 ◽  
Author(s):  
M. Karimi-Ghartemani ◽  
A.K. Ziarani
Keyword(s):  
Frequency Analysis ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency

scholarly journals SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4457 ◽  
Author(s):  
She ◽  
Zhu ◽  
Tian ◽  
Wang ◽  
Yokoi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Hand Movement ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Movement Recognition ◽  
S Transform

Feature extraction, as an important method for extracting useful information from surfaceelectromyography (SEMG), can significantly improve pattern recognition accuracy. Time andfrequency analysis methods have been widely used for feature extraction, but these methods analyzeSEMG signals only from the time or frequency domain. Recent studies have shown that featureextraction based on time-frequency analysis methods can extract more useful information fromSEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwelltransform (S-transform) to improve hand movement recognition accuracy from forearm SEMGsignals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vectorfrom forearm SEMG signals. Second, to reduce the amount of calculations and improve the runningspeed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of thefeature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is usedfor recognizing hand movements. Experimental results show that the proposed feature extractionbased on the S-transform analysis method can improve the class separability and hand movementrecognition accuracy compared with wavelet transform and power spectral density methods.

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